# import necessary  libraries 
import pandas as pd
import numpy as np 

import matplotlib.pyplot as plt 
import plotly.express as px
import seaborn as sns
import pycountry
%matplotlib inline
#offline- mode
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
# Importing high-level chart objects
import plotly.graph_objs as go
# Import tools
from plotly import tools
import cufflinks as cf

df = pd.read_csv('C://Users/Akara/Downloads/countries of the world.csv',decimal=',')

df.head(10)

	Country	Region	Population	Area (sq. mi.)	Pop. Density (per sq. mi.)	Coastline (coast/area ratio)	Net migration	Infant mortality (per 1000 births)	GDP ($ per capita)	Literacy (%)	Phones (per 1000)	Arable (%)	Crops (%)	Other (%)	Climate	Birthrate	Deathrate	Agriculture	Industry	Service
0	Afghanistan	ASIA (EX. NEAR EAST)	31056997	647500	48.0	0.00	23.06	163.07	700.0	36.0	3.2	12.13	0.22	87.65	1.0	46.60	20.34	0.380	0.240	0.380
1	Albania	EASTERN EUROPE	3581655	28748	124.6	1.26	-4.93	21.52	4500.0	86.5	71.2	21.09	4.42	74.49	3.0	15.11	5.22	0.232	0.188	0.579
2	Algeria	NORTHERN AFRICA	32930091	2381740	13.8	0.04	-0.39	31.00	6000.0	70.0	78.1	3.22	0.25	96.53	1.0	17.14	4.61	0.101	0.600	0.298
3	American Samoa	OCEANIA	57794	199	290.4	58.29	-20.71	9.27	8000.0	97.0	259.5	10.00	15.00	75.00	2.0	22.46	3.27	NaN	NaN	NaN
4	Andorra	WESTERN EUROPE	71201	468	152.1	0.00	6.60	4.05	19000.0	100.0	497.2	2.22	0.00	97.78	3.0	8.71	6.25	NaN	NaN	NaN
5	Angola	SUB-SAHARAN AFRICA	12127071	1246700	9.7	0.13	0.00	191.19	1900.0	42.0	7.8	2.41	0.24	97.35	NaN	45.11	24.20	0.096	0.658	0.246
6	Anguilla	LATIN AMER. & CARIB	13477	102	132.1	59.80	10.76	21.03	8600.0	95.0	460.0	0.00	0.00	100.00	2.0	14.17	5.34	0.040	0.180	0.780
7	Antigua & Barbuda	LATIN AMER. & CARIB	69108	443	156.0	34.54	-6.15	19.46	11000.0	89.0	549.9	18.18	4.55	77.27	2.0	16.93	5.37	0.038	0.220	0.743
8	Argentina	LATIN AMER. & CARIB	39921833	2766890	14.4	0.18	0.61	15.18	11200.0	97.1	220.4	12.31	0.48	87.21	3.0	16.73	7.55	0.095	0.358	0.547
9	Armenia	C.W. OF IND. STATES	2976372	29800	99.9	0.00	-6.47	23.28	3500.0	98.6	195.7	17.55	2.30	80.15	4.0	12.07	8.23	0.239	0.343	0.418

df2 = df.iloc[:,2:]

df2

	Population	Area (sq. mi.)	Pop. Density (per sq. mi.)	Coastline (coast/area ratio)	Net migration	Infant mortality (per 1000 births)	GDP ($ per capita)	Literacy (%)	Phones (per 1000)	Arable (%)	Crops (%)	Other (%)	Climate	Birthrate	Deathrate	Agriculture	Industry	Service
0	31056997	647500	48.0	0.00	23.06	163.07	700.0	36.0	3.2	12.13	0.22	87.65	1.0	46.60	20.34	0.380	0.240	0.380
1	3581655	28748	124.6	1.26	-4.93	21.52	4500.0	86.5	71.2	21.09	4.42	74.49	3.0	15.11	5.22	0.232	0.188	0.579
2	32930091	2381740	13.8	0.04	-0.39	31.00	6000.0	70.0	78.1	3.22	0.25	96.53	1.0	17.14	4.61	0.101	0.600	0.298
3	57794	199	290.4	58.29	-20.71	9.27	8000.0	97.0	259.5	10.00	15.00	75.00	2.0	22.46	3.27	NaN	NaN	NaN
4	71201	468	152.1	0.00	6.60	4.05	19000.0	100.0	497.2	2.22	0.00	97.78	3.0	8.71	6.25	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
222	2460492	5860	419.9	0.00	2.98	19.62	800.0	NaN	145.2	16.90	18.97	64.13	3.0	31.67	3.92	0.090	0.280	0.630
223	273008	266000	1.0	0.42	NaN	NaN	NaN	NaN	NaN	0.02	0.00	99.98	1.0	NaN	NaN	NaN	NaN	0.400
224	21456188	527970	40.6	0.36	0.00	61.50	800.0	50.2	37.2	2.78	0.24	96.98	1.0	42.89	8.30	0.135	0.472	0.393
225	11502010	752614	15.3	0.00	0.00	88.29	800.0	80.6	8.2	7.08	0.03	92.90	2.0	41.00	19.93	0.220	0.290	0.489
226	12236805	390580	31.3	0.00	0.00	67.69	1900.0	90.7	26.8	8.32	0.34	91.34	2.0	28.01	21.84	0.179	0.243	0.579

227 rows × 18 columns

print('no of rows:{}'.format(df.shape[0]),'no of columns:{}'.format(df.shape[1]))

no of rows:227 no of columns:20

df.isnull().sum()

Country                                0
Region                                 0
Population                             0
Area (sq. mi.)                         0
Pop. Density (per sq. mi.)             0
Coastline (coast/area ratio)           0
Net migration                          3
Infant mortality (per 1000 births)     3
GDP ($ per capita)                     1
Literacy (%)                          18
Phones (per 1000)                      4
Arable (%)                             2
Crops (%)                              2
Other (%)                              2
Climate                               22
Birthrate                              3
Deathrate                              4
Agriculture                           15
Industry                              16
Service                               15
dtype: int64

df.columns

Index(['Country', 'Region', 'Population', 'Area (sq. mi.)',
       'Pop. Density (per sq. mi.)', 'Coastline (coast/area ratio)',
       'Net migration', 'Infant mortality (per 1000 births)',
       'GDP ($ per capita)', 'Literacy (%)', 'Phones (per 1000)', 'Arable (%)',
       'Crops (%)', 'Other (%)', 'Climate', 'Birthrate', 'Deathrate',
       'Agriculture', 'Industry', 'Service'],
      dtype='object')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 227 entries, 0 to 226
Data columns (total 20 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   Country                             227 non-null    object 
 1   Region                              227 non-null    object 
 2   Population                          227 non-null    int64  
 3   Area (sq. mi.)                      227 non-null    int64  
 4   Pop. Density (per sq. mi.)          227 non-null    float64
 5   Coastline (coast/area ratio)        227 non-null    float64
 6   Net migration                       224 non-null    float64
 7   Infant mortality (per 1000 births)  224 non-null    float64
 8   GDP ($ per capita)                  226 non-null    float64
 9   Literacy (%)                        209 non-null    float64
 10  Phones (per 1000)                   223 non-null    float64
 11  Arable (%)                          225 non-null    float64
 12  Crops (%)                           225 non-null    float64
 13  Other (%)                           225 non-null    float64
 14  Climate                             205 non-null    float64
 15  Birthrate                           224 non-null    float64
 16  Deathrate                           223 non-null    float64
 17  Agriculture                         212 non-null    float64
 18  Industry                            211 non-null    float64
 19  Service                             212 non-null    float64
dtypes: float64(16), int64(2), object(2)
memory usage: 35.6+ KB

## creating an iso_alpha code of countries of easy use of visualizations
def do_fuzzy_search(country):
    try:
        result = pycountry.countries.search_fuzzy(country)
        return result[0].alpha_3
    except:
        return np.nan

df["country_code"] = df["Country"].apply(lambda country: do_fuzzy_search(country))

# TO KNOW THE MISSING VALUE PERCENT
print("missing percent is:")
def missing_percent(df):
    nan_percent= 100*(df.isnull().sum()/len(df))
    nan_percent= nan_percent[nan_percent>0].sort_values()
    return nan_percent
missing_percent(df)

missing percent is:

GDP ($ per capita)                     0.440529
Arable (%)                             0.881057
Crops (%)                              0.881057
Other (%)                              0.881057
Net migration                          1.321586
Infant mortality (per 1000 births)     1.321586
Birthrate                              1.321586
Phones (per 1000)                      1.762115
Deathrate                              1.762115
Agriculture                            6.607930
Service                                6.607930
Industry                               7.048458
Literacy (%)                           7.929515
Climate                                9.691630
country_code                          11.453744
dtype: float64

cols = df[['Net migration', 'Infant mortality (per 1000 births)',
       'GDP ($ per capita)', 'Literacy (%)', 'Phones (per 1000)', 'Arable (%)',
       'Crops (%)', 'Other (%)', 'Climate', 'Birthrate', 'Deathrate',
       'Agriculture', 'Industry', 'Service']]
for i in cols:
    df[i].fillna(df[i].mean(axis=0), inplace=True)

df.isnull().sum()

Country                                0
Region                                 0
Population                             0
Area (sq. mi.)                         0
Pop. Density (per sq. mi.)             0
Coastline (coast/area ratio)           0
Net migration                          0
Infant mortality (per 1000 births)     0
GDP ($ per capita)                     0
Literacy (%)                           0
Phones (per 1000)                      0
Arable (%)                             0
Crops (%)                              0
Other (%)                              0
Climate                                0
Birthrate                              0
Deathrate                              0
Agriculture                            0
Industry                               0
Service                                0
country_code                          26
dtype: int64

df.rename(columns={'Literacy (%)': 'Literacy',
                  'Arable (%)':'Arable',
                  'Crops (%)':'Crops',
                  'Other (%)':'Other_factors',
                  'Pop. Density (per sq. mi.)':'Population_density',
                  'Coastline (coast/area ratio)':'Coastline',
                  'Area (sq. mi.)':'Area_land',
                  'Infant mortality (per 1000 births)':'Infant_mortality',
                  'GDP ($ per capita)':'GDP_PCP',
                  'Phones (per 1000)':'Phones'},inplace=True)

df.round(4)

	Country	Region	Population	Area_land	Population_density	Coastline	Net migration	Infant_mortality	GDP_PCP	Literacy	...	Arable	Crops	Other_factors	Climate	Birthrate	Deathrate	Agriculture	Industry	Service	country_code
0	Afghanistan	ASIA (EX. NEAR EAST)	31056997	647500	48.0	0.00	23.0600	163.070	700.000	36.0000	...	12.13	0.22	87.65	1.0	46.6000	20.3400	0.3800	0.2400	0.3800	AFG
1	Albania	EASTERN EUROPE	3581655	28748	124.6	1.26	-4.9300	21.520	4500.000	86.5000	...	21.09	4.42	74.49	3.0	15.1100	5.2200	0.2320	0.1880	0.5790	ALB
2	Algeria	NORTHERN AFRICA	32930091	2381740	13.8	0.04	-0.3900	31.000	6000.000	70.0000	...	3.22	0.25	96.53	1.0	17.1400	4.6100	0.1010	0.6000	0.2980	DZA
3	American Samoa	OCEANIA	57794	199	290.4	58.29	-20.7100	9.270	8000.000	97.0000	...	10.00	15.00	75.00	2.0	22.4600	3.2700	0.1508	0.2827	0.5653	ASM
4	Andorra	WESTERN EUROPE	71201	468	152.1	0.00	6.6000	4.050	19000.000	100.0000	...	2.22	0.00	97.78	3.0	8.7100	6.2500	0.1508	0.2827	0.5653	AND
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
222	West Bank	NEAR EAST	2460492	5860	419.9	0.00	2.9800	19.620	800.000	82.8383	...	16.90	18.97	64.13	3.0	31.6700	3.9200	0.0900	0.2800	0.6300	NaN
223	Western Sahara	NORTHERN AFRICA	273008	266000	1.0	0.42	0.0381	35.507	9689.823	82.8383	...	0.02	0.00	99.98	1.0	22.1147	9.2413	0.1508	0.2827	0.4000	ESH
224	Yemen	NEAR EAST	21456188	527970	40.6	0.36	0.0000	61.500	800.000	50.2000	...	2.78	0.24	96.98	1.0	42.8900	8.3000	0.1350	0.4720	0.3930	YEM
225	Zambia	SUB-SAHARAN AFRICA	11502010	752614	15.3	0.00	0.0000	88.290	800.000	80.6000	...	7.08	0.03	92.90	2.0	41.0000	19.9300	0.2200	0.2900	0.4890	ZMB
226	Zimbabwe	SUB-SAHARAN AFRICA	12236805	390580	31.3	0.00	0.0000	67.690	1900.000	90.7000	...	8.32	0.34	91.34	2.0	28.0100	21.8400	0.1790	0.2430	0.5790	ZWE

227 rows × 21 columns

df.describe()

	Population	Area_land	Population_density	Coastline	Net migration	Infant_mortality	GDP_PCP	Literacy	Phones	Arable	Crops	Other_factors	Climate	Birthrate	Deathrate	Agriculture	Industry	Service
count	2.270000e+02	2.270000e+02	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000	227.000000
mean	2.874028e+07	5.982270e+05	379.047137	21.165330	0.038125	35.506964	9689.823009	82.838278	236.061435	13.797111	4.564222	81.638311	2.139024	22.114732	9.241345	0.150844	0.282711	0.565283
std	1.178913e+08	1.790282e+06	1660.185825	72.286863	4.856710	35.154225	10026.881258	18.920483	225.965194	12.982573	8.324390	16.069256	0.664484	11.102286	4.945670	0.141843	0.133288	0.160243
min	7.026000e+03	2.000000e+00	0.000000	0.000000	-20.990000	2.290000	500.000000	17.600000	0.200000	0.000000	0.000000	33.330000	1.000000	7.290000	2.290000	0.000000	0.020000	0.062000
25%	4.376240e+05	4.647500e+03	29.150000	0.100000	-0.905000	8.215000	1900.000000	76.400000	38.500000	3.380000	0.190000	71.985000	2.000000	12.725000	5.975000	0.040000	0.200000	0.443500
50%	4.786994e+06	8.660000e+04	78.800000	0.730000	0.000000	21.050000	5600.000000	90.300000	181.600000	10.530000	1.080000	85.380000	2.000000	18.900000	8.170000	0.116000	0.282000	0.565283
75%	1.749777e+07	4.418110e+05	190.150000	10.345000	0.980000	55.335000	15700.000000	97.800000	382.900000	20.000000	4.557111	95.230000	2.319512	29.770000	10.580000	0.211000	0.335000	0.671500
max	1.313974e+09	1.707520e+07	16271.500000	870.660000	23.060000	191.190000	55100.000000	100.000000	1035.600000	62.110000	50.680000	100.000000	4.000000	50.730000	29.740000	0.769000	0.906000	0.954000

#df['Literacy']= df.apply(lambda x: x.Literacy * 100,axis=1)
#df['Crops']= df.apply(lambda x: x.Crops * 100,axis=1)
#df['Others_factors']= df.apply(lambda x: x.Other_factors * 100,axis=1)
#df['Arable']= df.apply(lambda x: x.Arable * 100,axis=1)

VISUALIZATION

# visualization # on the net migration progress # visualize on 1the death rates 2.birth rates 3.infant morality (per 1000 births) 4.Industry 5.death rates 6.Pop. Density (per sq. mi.) 7.phone(per 1000) 8.Agriculture 9.climate 10.crops

# CORRELATIONS OF THE DATASET
plt.figure(figsize=(30,20))
sns.pairplot(df2)
plt.show()

<Figure size 2160x1440 with 0 Axes>

# CORRELATION ALSO
plt.figure(figsize=(16,12))
sns.heatmap(data=df.iloc[:,2:].corr(),annot=True,fmt='.2f',cmap='coolwarm')
plt.show()

top_deathrates=df.nlargest(10,['Net migration'] , keep='all')
#top_deathrates

fig=px.bar(top_deathrates , y='Net migration',x='Country',title='TOP TEN NET MIGRATION COUNTRIES',text='Deathrate',
          color_discrete_sequence=px.colors.sequential.Magma_r)
fig.update_traces(texttemplate="%{text:.2s}" ,textposition='outside')

top_population_coun=df.nlargest(20,'Population')
last_population_coun=df.nsmallest(20,'Population')
top_last_population_coun=pd.concat([top_population_coun,last_population_coun])
#top_population_coun

fig = px.bar(top_population_coun ,y ='Population',x = 'Country', text='Population',title='THE TOP POPULATED COUNTRIES',
            color_discrete_sequence=px.colors.sequential.Darkmint,template='plotly_dark')
fig.update_traces(texttemplate="%{text:.2s}" ,textposition='outside')
fig.update_layout(uniformtext_minsize=8)
fig.update_layout(xaxis_tickangle=-45)
fig

fig = px.bar(last_population_coun ,y ='Population',x = 'Country', text='Population',title='THE LAST POPULATED COUNTRIES')
fig.update_traces(texttemplate="%{text:.2s}" ,textposition='outside')
fig.update_traces(marker_color='rgb(267,006,225)', marker_line_color='rgb(200,408,127)',
                  marker_line_width=1.5, opacity=0.6)
fig.update_layout(uniformtext_minsize=8)
fig.update_layout(xaxis_tickangle=-45)
fig

px.bar(df, x='Region',y='Coastline',title='ANALTSIS ON THE COASTLINE COUNTRIES ACCORDING TO REGION',
      hover_name='Country',color_discrete_sequence=px.colors.sequential.Jet,log_y=True,)

fig = px.scatter_geo(df, locations='country_code',hover_name='Country',
                    size='Arable', projection='stereographic',title='THE ARABLE LAND WORLDWIDE COUNTRY VISUAL',
                    color_discrete_sequence=px.colors.sequential.gray,template='plotly',)
fig

fig= px.choropleth(df,locations='country_code',color='Arable',color_continuous_scale=px.colors.sequential.Cividis_r,
                   scope='europe',title='further analysis on the europe arable land',hover_name='Country')
fig

fig = px.sunburst(df, path=['Region', 'Country'], values='Population',
                  color='Area_land', hover_data=['country_code'],
                  color_continuous_scale='Oryel',
                  color_continuous_midpoint=np.average(df['Area_land'], weights=df['Population']),
                 title='Population and Area (square miles)(640arce==1area[square miles])')
fig.show()

fig = px.choropleth(df,locations='country_code',color='Climate',
                   color_continuous_scale='oxy',range_color=(0,12),hover_name='Country',
                   title='GLOBAL CLIMATE(%) ANALYSIS',projection='mollweide')
fig

#df3.rename({'df.Infant_mortality(per 1000 births)':'infant_mortality'},inplace=True)
trace0 = go.Scatter(x =df.Infant_mortality,
                  y =df.Birthrate,
                  mode = "markers",
                   name = "Birthrate(%)",
                  marker = dict(size = 12, color = "rgba(255, 70, 0, 0.9)"),xaxis='x1',yaxis='y2')

trace1 = go.Scatter(x =df.Infant_mortality,
                y =df.Deathrate,
                name = "Deathrate(%)",
                 mode = "markers",
                marker = dict(size = 12, color = "rgba(0, 190, 255, 0.9)"),xaxis='x2',yaxis='y2')

fig = tools.make_subplots(rows = 1,
                          cols = 2,
                         subplot_titles = ("birthrate", "deathrate"),)

fig.append_trace(trace0, 1, 1)
fig.append_trace(trace1, 1, 2)

fig.layout.update(title = "Correlation between infant_morality ,birthrate and Deathrate")

iplot(fig);

C:\Users\Akara\anaconda3\lib\site-packages\plotly\tools.py:460: DeprecationWarning:

plotly.tools.make_subplots is deprecated, please use plotly.subplots.make_subplots instead

#dir(px.colors.sequential)
#dir(px.colors.qualitative)

#pie chart
#infant morality
fig = px.pie(df, values='Infant_mortality', names='Region',color_discrete_sequence=px.colors.sequential.BuGn,
             template='plotly_dark',
             title='piechart depicting(the number of deaths per 1,000 live births of children under the age of one year)in the region')
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=12, uniformtext_mode=False,)
fig.show()

#donutchart
#deathrate
px.pie(df,values='Deathrate',names='Region',hole=0.5,color_discrete_sequence=px.colors.sequential.Burgyl,
       template='plotly_dark',title='A DONUT CHART DEATHRATE ANALYSIS')

px.box(df['GDP_PCP'],hover_name=df['Country'] ,template='plotly_dark',title='A boxplot on the GDP',
       color_discrete_sequence=px.colors.sequential.Burgyl)

high_GDP =df.nlargest(15,'GDP_PCP', keep='all')
low_GDP =df.nsmallest(15,'GDP_PCP')

px.bar(high_GDP ,y='Country',x='GDP_PCP',color_discrete_sequence=px.colors.sequential.Rainbow,template='plotly',
      text_auto='GDP_PCP',orientation='h',title='A BAR CHART ON THE TOP TEN GDP COUNTRIES')

px.bar(low_GDP ,y='Country',x='GDP_PCP',color_discrete_sequence=px.colors.sequential.Cividis,template='plotly',
      text_auto='GDP_PCP',orientation='h',title='A BAR CHART ON LAST GDP COUNTRIES')

#industry
g=df['Industry'].max()
f=df['Industry'].min()
high_con_indu =df.nlargest(20,'Industry')
fig = px.scatter(df ,x='Country',y='Industry',size='Industry',color_discrete_sequence=px.colors.sequential.Viridis,
           log_y=True,range_y=[0.01,2],marginal_x='box',title='INDUSTRIES ANALYSIS ON COUNTRIES',width=1000,
          height=700)

fig.show()
fig.write_html('C://Users/Akara/Downloads/industries.html')

#service
#df['Service'].max()
df['Service'].min()
px.area(df,y='Service',x='Country',hover_name='Country',color_discrete_sequence=px.colors.sequential.gray_r,
        markers=True,template='plotly_dark',title='A SERVICE AREA CHART BY COUNTRIES')

#LITERACY
px.histogram(df, x='Country',y='Literacy',height=600,width=1000,marginal='violin',
             color_discrete_sequence=px.colors.sequential.Sunsetdark,template='plotly_dark',
            title='LITERACY ANALYSIS ON COUNTRIES',range_y=[20,100])

#Agriculture
px.violin(df,y='Agriculture',box=True,points='all',color_discrete_sequence=px.colors.sequential.Plasma_r,
          template='plotly_dark',title='A VIOLIN ON AGRICULTURE BASED ON COUNTRIES',hover_name='Country')

px.area(df,x='Country',y='Population_density',log_y=True,markers=True,
        color_discrete_sequence=px.colors.sequential.Aggrnyl,title='A AREA CHART ON THE POPULATION DENSITY')

fig=px.choropleth(df,locations='country_code',color='Population_density',basemap_visible=True,
             color_continuous_scale=px.colors.sequential.solar,projection='robinson',scope='africa',
             range_color=[0,51],title='A AFRICA CHOROPLETH MAP ON THE POPULATION DENSITY')
fig.show()

MACHINE LEARNING ANALYSIS

GDP=df.corr()['GDP_PCP']
GDP_CORR=pd.DataFrame(GDP)
cm1 = sns.light_palette('green',as_cmap=True)
GDP_CORR.style.background_gradient(cmap=cm1)

	GDP_PCP
Population	-0.039319
Area_land	0.072179
Population_density	0.195751
Coastline	0.049109
Net migration	0.381969
Infant_mortality	-0.600002
GDP_PCP	1.000000
Literacy	0.497963
Phones	0.830549
Arable	0.019643
Crops	-0.218330
Other_factors	0.097215
Climate	0.302404
Birthrate	-0.648808
Deathrate	-0.201148
Agriculture	-0.570735
Industry	-0.027935
Service	0.529995

#machine learnig dataframe copy
df3=pd.DataFrame(df)

gdp_per_capita=df['GDP_PCP']
df3 =df3.drop(['Country', 'Region', 'Population','Area_land','Birthrate', 'Deathrate',
       'Agriculture', 'Industry', 'country_code','Coastline','Infant_mortality', 'Arable',
       'Crops', 'Other_factors','GDP_PCP','Climate'],axis=1)
df3['GDP_per_capita($)']=gdp_per_capita

df3

	Population_density	Net migration	Literacy	Phones	Service	GDP_per_capita($)
0	48.0	23.060000	36.000000	3.200000	0.380000	700.000000
1	124.6	-4.930000	86.500000	71.200000	0.579000	4500.000000
2	13.8	-0.390000	70.000000	78.100000	0.298000	6000.000000
3	290.4	-20.710000	97.000000	259.500000	0.565283	8000.000000
4	152.1	6.600000	100.000000	497.200000	0.565283	19000.000000
...	...	...	...	...	...	...
222	419.9	2.980000	82.838278	145.200000	0.630000	800.000000
223	1.0	0.038125	82.838278	236.061435	0.400000	9689.823009
224	40.6	0.000000	50.200000	37.200000	0.393000	800.000000
225	15.3	0.000000	80.600000	8.200000	0.489000	800.000000
226	31.3	0.000000	90.700000	26.800000	0.579000	1900.000000

227 rows × 6 columns

X  = df[['Phones']].to_numpy()
y = df[['GDP_PCP']].to_numpy()
# splitting into training and test data
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test =train_test_split(X,y,test_size=0.3,random_state=0)


#train the model on training set
from sklearn.linear_model import LinearRegression
ML= LinearRegression()
ML.fit(X_train,y_train)

y_pred =ML.predict(X_test)
from sklearn.metrics import r2_score

r2_score(y_test,y_pred)*100
 

73.10225063724188

import statsmodels.formula.api as smpi
zl=smpi.ols(formula='Phones~GDP_PCP',data=df).fit()
zl.summary()

OLS Regression Results

Dep. Variable:	Phones	R-squared:	0.690
Model:	OLS	Adj. R-squared:	0.688
Method:	Least Squares	F-statistic:	500.4
Date:	Mon, 01 Aug 2022	Prob (F-statistic):	4.10e-59
Time:	12:23:19	Log-Likelihood:	-1419.2
No. Observations:	227	AIC:	2842.
Df Residuals:	225	BIC:	2849.
Df Model:	1
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
Intercept	54.6951	11.654	4.693	0.000	31.730	77.661
GDP_PCP	0.0187	0.001	22.369	0.000	0.017	0.020

Omnibus:	53.457	Durbin-Watson:	1.795
Prob(Omnibus):	0.000	Jarque-Bera (JB):	205.452
Skew:	0.890	Prob(JB):	2.44e-45
Kurtosis:	7.307	Cond. No.	1.94e+04

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.94e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

#ML.predict([[]])

machine learning predictions on infant mortality

#linear multiple regression
IFM=df.corr()['Infant_mortality']
IFM_CORR =pd.DataFrame(IFM)

cm = sns.light_palette("orange", as_cmap=True)

IFM_CORR.style.background_gradient(cmap=cm)

	Infant_mortality
Population	0.022994
Area_land	-0.007159
Population_density	-0.144200
Coastline	-0.136465
Net migration	-0.025015
Infant_mortality	1.000000
GDP_PCP	-0.600002
Literacy	-0.745543
Phones	-0.669715
Arable	-0.113094
Crops	-0.061159
Other_factors	0.123072
Climate	-0.342848
Birthrate	0.844968
Deathrate	0.655734
Agriculture	0.697303
Industry	0.003510
Service	-0.618623

#y = mx +c
# Generally: y[i] = alpha + (beta_1 * x_1[i]) + (beta_2 * x_2[i]) + (beta_3 * x_3[i]) + error
# Model:     y_hat[i] = alpha_hat + (beta_1_hat * x_1[i]) + (beta_2_hat * x_2[i]) + (beta_3_hat * x_3[i])
#alpha_hat = y_intercept = C
X = df[['Birthrate']].to_numpy()
y= df[['Infant_mortality']].to_numpy()
tl = LinearRegression()
tl.fit(X,y)

LinearRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

y_pred=tl.predict(X)
plt.scatter(X,y)
plt.plot(X,y_pred ,color='r')

[<matplotlib.lines.Line2D at 0x1e269e3d3d0>]

print(tl.coef_)

[[2.67550327]]

print(tl.intercept_)

[-23.66107379]

from sklearn.metrics import r2_score
r2_score(y,y_pred)

0.7139712734799849

# machine learning predictions on infant  mortality

X=df[['Birthrate','Agriculture','Deathrate']].to_numpy()
y=df[['Infant_mortality']].to_numpy()

X

array([[4.66000000e+01, 3.80000000e-01, 2.03400000e+01],
       [1.51100000e+01, 2.32000000e-01, 5.22000000e+00],
       [1.71400000e+01, 1.01000000e-01, 4.61000000e+00],
       [2.24600000e+01, 1.50844340e-01, 3.27000000e+00],
       [8.71000000e+00, 1.50844340e-01, 6.25000000e+00],
       [4.51100000e+01, 9.60000000e-02, 2.42000000e+01],
       [1.41700000e+01, 4.00000000e-02, 5.34000000e+00],
       [1.69300000e+01, 3.80000000e-02, 5.37000000e+00],
       [1.67300000e+01, 9.50000000e-02, 7.55000000e+00],
       [1.20700000e+01, 2.39000000e-01, 8.23000000e+00],
       [1.10300000e+01, 4.00000000e-03, 6.68000000e+00],
       [1.21400000e+01, 3.80000000e-02, 7.51000000e+00],
       [8.74000000e+00, 1.80000000e-02, 9.76000000e+00],
       [2.07400000e+01, 1.41000000e-01, 9.75000000e+00],
       [1.75700000e+01, 3.00000000e-02, 9.05000000e+00],
       [1.78000000e+01, 5.00000000e-03, 4.14000000e+00],
       [2.98000000e+01, 1.99000000e-01, 8.27000000e+00],
       [1.27100000e+01, 6.00000000e-02, 8.67000000e+00],
       [1.11600000e+01, 9.30000000e-02, 1.40200000e+01],
       [1.03800000e+01, 1.00000000e-02, 1.02700000e+01],
       [2.88400000e+01, 1.42000000e-01, 5.72000000e+00],
       [3.88500000e+01, 3.16000000e-01, 1.22200000e+01],
       [1.14000000e+01, 1.00000000e-02, 7.74000000e+00],
       [3.36500000e+01, 2.58000000e-01, 1.27000000e+01],
       [2.33000000e+01, 1.28000000e-01, 7.53000000e+00],
       [8.77000000e+00, 1.42000000e-01, 8.27000000e+00],
       [2.30800000e+01, 2.40000000e-02, 2.95000000e+01],
       [1.65600000e+01, 8.40000000e-02, 6.17000000e+00],
       [1.48900000e+01, 1.80000000e-02, 4.42000000e+00],
       [1.87900000e+01, 3.60000000e-02, 3.45000000e+00],
       [9.65000000e+00, 9.30000000e-02, 1.42700000e+01],
       [4.56200000e+01, 3.22000000e-01, 1.56000000e+01],
       [1.79100000e+01, 5.64000000e-01, 9.83000000e+00],
       [4.22200000e+01, 4.63000000e-01, 1.34600000e+01],
       [2.69000000e+01, 3.50000000e-01, 9.06000000e+00],
       [3.38900000e+01, 4.48000000e-01, 1.34700000e+01],
       [1.07800000e+01, 2.20000000e-02, 7.80000000e+00],
       [2.48700000e+01, 1.21000000e-01, 6.55000000e+00],
       [1.27400000e+01, 1.40000000e-02, 4.89000000e+00],
       [3.39100000e+01, 5.50000000e-01, 1.86500000e+01],
       [4.57300000e+01, 3.35000000e-01, 1.63800000e+01],
       [1.52300000e+01, 6.00000000e-02, 5.81000000e+00],
       [1.32500000e+01, 1.25000000e-01, 6.97000000e+00],
       [2.04800000e+01, 1.25000000e-01, 5.58000000e+00],
       [3.69300000e+01, 4.00000000e-01, 8.20000000e+00],
       [4.36900000e+01, 5.50000000e-01, 1.32700000e+01],
       [4.25700000e+01, 6.20000000e-02, 1.29300000e+01],
       [2.10000000e+01, 1.51000000e-01, 9.24134529e+00],
       [1.83200000e+01, 8.80000000e-02, 4.36000000e+00],
       [3.51100000e+01, 2.79000000e-01, 1.48400000e+01],
       [9.61000000e+00, 7.00000000e-02, 1.14800000e+01],
       [1.18900000e+01, 5.50000000e-02, 7.22000000e+00],
       [1.25600000e+01, 3.70000000e-02, 7.68000000e+00],
       [9.02000000e+00, 3.40000000e-02, 1.05900000e+01],
       [1.11300000e+01, 1.80000000e-02, 1.03600000e+01],
       [3.95300000e+01, 1.79000000e-01, 1.93100000e+01],
       [1.52700000e+01, 1.77000000e-01, 6.73000000e+00],
       [2.32200000e+01, 1.12000000e-01, 5.73000000e+00],
       [2.69900000e+01, 8.50000000e-02, 6.24000000e+00],
       [2.22900000e+01, 7.00000000e-02, 4.23000000e+00],
       [2.29400000e+01, 1.49000000e-01, 5.23000000e+00],
       [2.66100000e+01, 9.90000000e-02, 5.78000000e+00],
       [3.55900000e+01, 3.00000000e-02, 1.50600000e+01],
       [3.43300000e+01, 1.02000000e-01, 9.60000000e+00],
       [1.00400000e+01, 4.00000000e-02, 1.32500000e+01],
       [3.79800000e+01, 4.75000000e-01, 1.48600000e+01],
       [1.40500000e+01, 2.70000000e-01, 8.70000000e+00],
       [2.25500000e+01, 8.90000000e-02, 5.65000000e+00],
       [1.04500000e+01, 2.80000000e-02, 9.86000000e+00],
       [1.19900000e+01, 2.20000000e-02, 9.14000000e+00],
       [2.04600000e+01, 6.60000000e-02, 4.88000000e+00],
       [1.66800000e+01, 3.10000000e-02, 4.69000000e+00],
       [3.61600000e+01, 6.10000000e-02, 1.22500000e+01],
       [3.93700000e+01, 3.08000000e-01, 1.22500000e+01],
       [3.94500000e+01, 3.00000000e-02, 3.80000000e+00],
       [1.04100000e+01, 1.72000000e-01, 9.23000000e+00],
       [8.25000000e+00, 9.00000000e-03, 1.06200000e+01],
       [3.05200000e+01, 3.66000000e-01, 9.72000000e+00],
       [1.07400000e+01, 1.50844340e-01, 9.31000000e+00],
       [9.68000000e+00, 5.40000000e-02, 1.02400000e+01],
       [1.59300000e+01, 1.50844340e-01, 7.84000000e+00],
       [2.20800000e+01, 5.40000000e-02, 6.88000000e+00],
       [1.50500000e+01, 1.50000000e-01, 6.09000000e+00],
       [1.87900000e+01, 1.50844340e-01, 4.48000000e+00],
       [2.98800000e+01, 2.27000000e-01, 5.20000000e+00],
       [8.81000000e+00, 3.00000000e-02, 1.00100000e+01],
       [4.17600000e+01, 2.37000000e-01, 1.54800000e+01],
       [3.72200000e+01, 6.20000000e-01, 1.65300000e+01],
       [1.82800000e+01, 3.70000000e-01, 8.28000000e+00],
       [3.64400000e+01, 2.80000000e-01, 1.21700000e+01],
       [2.82400000e+01, 1.39000000e-01, 5.28000000e+00],
       [7.29000000e+00, 1.00000000e-03, 6.29000000e+00],
       [9.72000000e+00, 3.70000000e-02, 1.31100000e+01],
       [1.36400000e+01, 8.60000000e-02, 6.72000000e+00],
       [2.20100000e+01, 1.86000000e-01, 8.18000000e+00],
       [2.03400000e+01, 1.34000000e-01, 6.25000000e+00],
       [1.70000000e+01, 1.16000000e-01, 5.55000000e+00],
       [3.19800000e+01, 7.30000000e-02, 5.37000000e+00],
       [1.44500000e+01, 5.00000000e-02, 7.82000000e+00],
       [1.10500000e+01, 1.00000000e-02, 1.11900000e+01],
       [1.79700000e+01, 2.60000000e-02, 6.18000000e+00],
       [8.72000000e+00, 2.10000000e-02, 1.04000000e+01],
       [2.08200000e+01, 4.90000000e-02, 6.52000000e+00],
       [9.37000000e+00, 1.70000000e-02, 9.16000000e+00],
       [9.30000000e+00, 5.00000000e-02, 9.28000000e+00],
       [2.12500000e+01, 3.30000000e-02, 2.65000000e+00],
       [1.60000000e+01, 6.70000000e-02, 9.42000000e+00],
       [3.97200000e+01, 1.63000000e-01, 1.40200000e+01],
       [3.06500000e+01, 8.90000000e-02, 8.26000000e+00],
       [1.55400000e+01, 3.00000000e-01, 7.13000000e+00],
       [1.00000000e+01, 3.30000000e-02, 5.85000000e+00],
       [2.19400000e+01, 4.00000000e-03, 2.41000000e+00],
       [2.28000000e+01, 3.53000000e-01, 7.08000000e+00],
       [3.54900000e+01, 4.55000000e-01, 1.15500000e+01],
       [9.24000000e+00, 4.00000000e-02, 1.36600000e+01],
       [1.85200000e+01, 1.20000000e-01, 6.21000000e+00],
       [2.47500000e+01, 1.63000000e-01, 2.87100000e+01],
       [4.47700000e+01, 7.69000000e-01, 2.31000000e+01],
       [2.64900000e+01, 7.60000000e-02, 3.48000000e+00],
       [1.02100000e+01, 6.00000000e-02, 7.18000000e+00],
       [8.75000000e+00, 5.50000000e-02, 1.09800000e+01],
       [1.19400000e+01, 1.00000000e-02, 8.41000000e+00],
       [8.48000000e+00, 1.00000000e-03, 4.47000000e+00],
       [1.20200000e+01, 1.18000000e-01, 8.77000000e+00],
       [4.14100000e+01, 2.76000000e-01, 1.11100000e+01],
       [4.31300000e+01, 3.42000000e-01, 1.93300000e+01],
       [2.28600000e+01, 8.40000000e-02, 5.05000000e+00],
       [3.48100000e+01, 2.00000000e-01, 7.06000000e+00],
       [4.98200000e+01, 4.50000000e-01, 1.68900000e+01],
       [1.02200000e+01, 3.00000000e-02, 8.10000000e+00],
       [3.30500000e+01, 3.17000000e-01, 4.78000000e+00],
       [1.37400000e+01, 6.00000000e-02, 6.48000000e+00],
       [4.09900000e+01, 2.50000000e-01, 1.21600000e+01],
       [1.54300000e+01, 5.90000000e-02, 6.86000000e+00],
       [4.09500000e+01, 1.50844340e-01, 7.70000000e+00],
       [2.06900000e+01, 3.80000000e-02, 4.74000000e+00],
       [2.46800000e+01, 2.89000000e-01, 4.75000000e+00],
       [1.57000000e+01, 2.13000000e-01, 1.26400000e+01],
       [9.19000000e+00, 1.70000000e-01, 1.29100000e+01],
       [2.15900000e+01, 2.06000000e-01, 6.95000000e+00],
       [1.75900000e+01, 1.50844340e-01, 7.10000000e+00],
       [2.19800000e+01, 2.17000000e-01, 5.58000000e+00],
       [3.51800000e+01, 2.62000000e-01, 2.13500000e+01],
       [2.43200000e+01, 9.70000000e-02, 1.88600000e+01],
       [2.47600000e+01, 1.50844340e-01, 6.70000000e+00],
       [3.09800000e+01, 3.80000000e-01, 9.31000000e+00],
       [1.09000000e+01, 2.10000000e-02, 8.68000000e+00],
       [1.47800000e+01, 1.00000000e-02, 6.45000000e+00],
       [1.81100000e+01, 1.50000000e-01, 5.69000000e+00],
       [1.37600000e+01, 4.30000000e-02, 7.53000000e+00],
       [2.45100000e+01, 1.65000000e-01, 4.45000000e+00],
       [5.07300000e+01, 3.90000000e-01, 2.09100000e+01],
       [4.04300000e+01, 2.69000000e-01, 1.69400000e+01],
       [1.94300000e+01, 1.50844340e-01, 2.29000000e+00],
       [1.14600000e+01, 2.10000000e-02, 9.40000000e+00],
       [3.62400000e+01, 2.70000000e-02, 3.81000000e+00],
       [2.97400000e+01, 2.16000000e-01, 8.23000000e+00],
       [1.80300000e+01, 6.20000000e-02, 6.80000000e+00],
       [2.17400000e+01, 6.80000000e-02, 5.36000000e+00],
       [2.93600000e+01, 3.53000000e-01, 7.25000000e+00],
       [2.91000000e+01, 2.24000000e-01, 4.49000000e+00],
       [2.04800000e+01, 8.00000000e-02, 6.23000000e+00],
       [2.48900000e+01, 1.44000000e-01, 5.41000000e+00],
       [9.85000000e+00, 5.00000000e-02, 9.89000000e+00],
       [1.07200000e+01, 5.30000000e-02, 1.05000000e+01],
       [1.27700000e+01, 1.00000000e-02, 7.65000000e+00],
       [1.55600000e+01, 2.00000000e-03, 4.72000000e+00],
       [1.89000000e+01, 8.00000000e-02, 5.49000000e+00],
       [1.07000000e+01, 1.01000000e-01, 1.17700000e+01],
       [9.95000000e+00, 5.40000000e-02, 1.46500000e+01],
       [4.03700000e+01, 4.01000000e-01, 1.60900000e+01],
       [1.21300000e+01, 1.50844340e-01, 6.53000000e+00],
       [1.80200000e+01, 3.50000000e-02, 8.33000000e+00],
       [1.96800000e+01, 7.00000000e-02, 5.08000000e+00],
       [1.35200000e+01, 1.50844340e-01, 6.83000000e+00],
       [1.61800000e+01, 1.00000000e-01, 5.98000000e+00],
       [1.64300000e+01, 1.14000000e-01, 6.62000000e+00],
       [1.00200000e+01, 1.50844340e-01, 8.17000000e+00],
       [4.02500000e+01, 1.67000000e-01, 6.47000000e+00],
       [2.93400000e+01, 3.30000000e-02, 2.58000000e+00],
       [3.27800000e+01, 1.72000000e-01, 9.42000000e+00],
       [2.21147321e+01, 1.66000000e-01, 9.24134529e+00],
       [1.60300000e+01, 3.20000000e-02, 6.29000000e+00],
       [4.57600000e+01, 4.90000000e-01, 2.30300000e+01],
       [9.34000000e+00, 0.00000000e+00, 4.28000000e+00],
       [1.06500000e+01, 3.50000000e-02, 9.45000000e+00],
       [8.98000000e+00, 2.80000000e-02, 1.03100000e+01],
       [3.00100000e+01, 4.20000000e-01, 3.92000000e+00],
       [4.51300000e+01, 6.50000000e-01, 1.66300000e+01],
       [1.82000000e+01, 2.50000000e-02, 2.20000000e+01],
       [1.00600000e+01, 4.00000000e-02, 9.72000000e+00],
       [1.55100000e+01, 1.78000000e-01, 6.52000000e+00],
       [3.45300000e+01, 3.87000000e-01, 8.97000000e+00],
       [1.80200000e+01, 1.30000000e-01, 7.27000000e+00],
       [2.74100000e+01, 1.19000000e-01, 2.97400000e+01],
       [1.02700000e+01, 1.10000000e-02, 1.03100000e+01],
       [9.71000000e+00, 1.50000000e-02, 8.49000000e+00],
       [2.77600000e+01, 2.49000000e-01, 4.81000000e+00],
       [1.25600000e+01, 1.80000000e-02, 6.48000000e+00],
       [3.26500000e+01, 2.34000000e-01, 8.25000000e+00],
       [3.77100000e+01, 4.32000000e-01, 1.63900000e+01],
       [1.38700000e+01, 9.90000000e-02, 7.04000000e+00],
       [3.70100000e+01, 3.95000000e-01, 9.83000000e+00],
       [2.53700000e+01, 2.30000000e-01, 5.28000000e+00],
       [1.29000000e+01, 7.00000000e-03, 1.05700000e+01],
       [1.55200000e+01, 1.32000000e-01, 5.13000000e+00],
       [1.66200000e+01, 1.17000000e-01, 5.97000000e+00],
       [2.76100000e+01, 2.09000000e-01, 8.60000000e+00],
       [2.18400000e+01, 1.50844340e-01, 4.21000000e+00],
       [2.21800000e+01, 1.66000000e-01, 7.11000000e+00],
       [4.73500000e+01, 3.11000000e-01, 1.22400000e+01],
       [8.82000000e+00, 1.87000000e-01, 1.43900000e+01],
       [1.89600000e+01, 4.00000000e-02, 4.40000000e+00],
       [1.07100000e+01, 5.00000000e-03, 1.01300000e+01],
       [1.41400000e+01, 1.00000000e-02, 8.26000000e+00],
       [1.39100000e+01, 9.30000000e-02, 9.05000000e+00],
       [2.63600000e+01, 3.42000000e-01, 7.84000000e+00],
       [2.27200000e+01, 2.60000000e-01, 7.82000000e+00],
       [1.87100000e+01, 4.00000000e-02, 4.92000000e+00],
       [1.68600000e+01, 2.09000000e-01, 6.22000000e+00],
       [1.39600000e+01, 1.00000000e-02, 6.43000000e+00],
       [2.21147321e+01, 1.50844340e-01, 9.24134529e+00],
       [3.16700000e+01, 9.00000000e-02, 3.92000000e+00],
       [2.21147321e+01, 1.50844340e-01, 9.24134529e+00],
       [4.28900000e+01, 1.35000000e-01, 8.30000000e+00],
       [4.10000000e+01, 2.20000000e-01, 1.99300000e+01],
       [2.80100000e+01, 1.79000000e-01, 2.18400000e+01]])

y

array([[163.07      ],
       [ 21.52      ],
       [ 31.        ],
       [  9.27      ],
       [  4.05      ],
       [191.19      ],
       [ 21.03      ],
       [ 19.46      ],
       [ 15.18      ],
       [ 23.28      ],
       [  5.89      ],
       [  4.69      ],
       [  4.66      ],
       [ 81.74      ],
       [ 25.21      ],
       [ 17.27      ],
       [ 62.6       ],
       [ 12.5       ],
       [ 13.37      ],
       [  4.68      ],
       [ 25.69      ],
       [ 85.        ],
       [  8.53      ],
       [100.44      ],
       [ 53.11      ],
       [ 21.05      ],
       [ 54.58      ],
       [ 29.61      ],
       [ 18.05      ],
       [ 12.61      ],
       [ 20.55      ],
       [ 97.57      ],
       [ 67.24      ],
       [ 69.29      ],
       [ 71.48      ],
       [ 68.26      ],
       [  4.75      ],
       [ 47.77      ],
       [  8.19      ],
       [ 91.        ],
       [ 93.82      ],
       [  8.8       ],
       [ 24.18      ],
       [ 20.97      ],
       [ 74.93      ],
       [ 94.69      ],
       [ 93.86      ],
       [ 35.50696429],
       [  9.95      ],
       [ 90.83      ],
       [  6.84      ],
       [  6.33      ],
       [  7.18      ],
       [  3.93      ],
       [  4.56      ],
       [104.13      ],
       [ 14.15      ],
       [ 32.38      ],
       [ 47.41      ],
       [ 23.66      ],
       [ 32.59      ],
       [ 25.1       ],
       [ 85.13      ],
       [ 74.87      ],
       [  7.87      ],
       [ 95.32      ],
       [  6.24      ],
       [ 12.62      ],
       [  3.57      ],
       [  4.26      ],
       [ 12.07      ],
       [  8.44      ],
       [ 53.64      ],
       [ 72.02      ],
       [ 22.93      ],
       [ 18.59      ],
       [  4.16      ],
       [ 51.43      ],
       [  5.13      ],
       [  5.53      ],
       [ 15.82      ],
       [ 14.62      ],
       [  8.6       ],
       [  6.94      ],
       [ 35.93      ],
       [  4.71      ],
       [ 90.37      ],
       [107.17      ],
       [ 33.26      ],
       [ 73.45      ],
       [ 29.32      ],
       [  2.97      ],
       [  8.57      ],
       [  3.31      ],
       [ 56.29      ],
       [ 35.6       ],
       [ 41.58      ],
       [ 50.25      ],
       [  5.39      ],
       [  5.93      ],
       [  7.03      ],
       [  5.94      ],
       [ 12.36      ],
       [  3.26      ],
       [  5.24      ],
       [ 17.35      ],
       [ 29.21      ],
       [ 61.47      ],
       [ 48.52      ],
       [ 24.04      ],
       [  7.05      ],
       [  9.95      ],
       [ 35.64      ],
       [ 85.22      ],
       [  9.55      ],
       [ 24.52      ],
       [ 84.23      ],
       [128.87      ],
       [ 24.6       ],
       [  4.7       ],
       [  6.89      ],
       [  4.81      ],
       [  4.39      ],
       [ 10.09      ],
       [ 76.83      ],
       [103.32      ],
       [ 17.7       ],
       [ 56.52      ],
       [116.79      ],
       [  3.89      ],
       [ 29.45      ],
       [  7.09      ],
       [ 70.89      ],
       [ 15.03      ],
       [ 62.4       ],
       [ 20.91      ],
       [ 30.21      ],
       [ 40.42      ],
       [  5.43      ],
       [ 53.79      ],
       [  7.35      ],
       [ 41.62      ],
       [130.79      ],
       [ 48.98      ],
       [  9.95      ],
       [ 66.98      ],
       [  5.04      ],
       [ 10.03      ],
       [  7.72      ],
       [  5.85      ],
       [ 29.11      ],
       [121.69      ],
       [ 98.8       ],
       [  7.11      ],
       [  3.7       ],
       [ 19.51      ],
       [ 72.44      ],
       [ 14.84      ],
       [ 20.47      ],
       [ 51.45      ],
       [ 25.63      ],
       [ 31.94      ],
       [ 23.51      ],
       [  8.51      ],
       [  5.05      ],
       [  8.24      ],
       [ 18.61      ],
       [  7.78      ],
       [ 26.43      ],
       [ 15.39      ],
       [ 91.23      ],
       [ 19.        ],
       [ 14.49      ],
       [ 13.53      ],
       [  7.54      ],
       [ 14.78      ],
       [ 27.71      ],
       [  5.73      ],
       [ 43.11      ],
       [ 13.24      ],
       [ 55.51      ],
       [ 12.89      ],
       [ 15.53      ],
       [143.64      ],
       [  2.29      ],
       [  7.41      ],
       [  4.45      ],
       [ 21.29      ],
       [116.7       ],
       [ 61.81      ],
       [  4.42      ],
       [ 14.35      ],
       [ 62.5       ],
       [ 23.57      ],
       [ 69.27      ],
       [  2.77      ],
       [  4.39      ],
       [ 29.53      ],
       [  6.4       ],
       [110.76      ],
       [ 98.54      ],
       [ 20.48      ],
       [ 66.61      ],
       [ 12.62      ],
       [ 24.31      ],
       [ 24.77      ],
       [ 41.04      ],
       [ 73.08      ],
       [ 15.67      ],
       [ 20.03      ],
       [ 67.83      ],
       [ 20.34      ],
       [ 14.51      ],
       [  5.16      ],
       [  6.5       ],
       [ 11.95      ],
       [ 71.1       ],
       [ 55.16      ],
       [ 22.2       ],
       [ 25.95      ],
       [  8.03      ],
       [ 35.50696429],
       [ 19.62      ],
       [ 35.50696429],
       [ 61.5       ],
       [ 88.29      ],
       [ 67.69      ]])

df.head()

	Country	Region	Population	Area_land	Population_density	Coastline	Net migration	Infant_mortality	GDP_PCP	Literacy	...	Arable	Crops	Other_factors	Climate	Birthrate	Deathrate	Agriculture	Industry	Service	country_code
0	Afghanistan	ASIA (EX. NEAR EAST)	31056997	647500	48.0	0.00	23.06	163.07	700.0	36.0	...	12.13	0.22	87.65	1.0	46.60	20.34	0.380000	0.240000	0.380000	AFG
1	Albania	EASTERN EUROPE	3581655	28748	124.6	1.26	-4.93	21.52	4500.0	86.5	...	21.09	4.42	74.49	3.0	15.11	5.22	0.232000	0.188000	0.579000	ALB
2	Algeria	NORTHERN AFRICA	32930091	2381740	13.8	0.04	-0.39	31.00	6000.0	70.0	...	3.22	0.25	96.53	1.0	17.14	4.61	0.101000	0.600000	0.298000	DZA
3	American Samoa	OCEANIA	57794	199	290.4	58.29	-20.71	9.27	8000.0	97.0	...	10.00	15.00	75.00	2.0	22.46	3.27	0.150844	0.282711	0.565283	ASM
4	Andorra	WESTERN EUROPE	71201	468	152.1	0.00	6.60	4.05	19000.0	100.0	...	2.22	0.00	97.78	3.0	8.71	6.25	0.150844	0.282711	0.565283	AND

5 rows × 21 columns

# splitting into training and test data
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test =train_test_split(X,y,test_size=0.3,random_state=0)

#train the model on training set
from sklearn.linear_model import LinearRegression
ML= LinearRegression()
ML.fit(X_train,y_train)

LinearRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

#predict the test set result
y_pred =ML.predict(X_test)
#print(y_predict)

#ML.predict([[46.60,38.000000,20.34]])

from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error,accuracy_score
r2_score(y_test,y_pred)*100

75.442300383338

plt.scatter(y_test,y_pred)

<matplotlib.collections.PathCollection at 0x1e269eb8af0>

print(ML.score(X_test,y_test)*100)
print(mean_squared_error(y_test,y_pred))
print(mean_absolute_error(y_test,y_pred))

75.442300383338
273.7885737129123
10.774000892106692

#ML.predict([[163]])